Demographics

Age Distribution

Full Sample (8-25 years)

Total N: 89
Total Females: 45
Total Males: 44
Mean: 16.1583808
SD: 4.6679792

Children (8-12 years)

Total N: 28
Total Females: 14
Total Males: 14
Mean: 11.0380626
SD: 1.2411494

Adolescents (13-17 years)

Total N: 31
Total Females: 16
Total Males: 15
Mean: 15.3458241
SD: 1.1924818

Adults (18-25 years)

Total N: 30
Total Females: 15
Total Males: 15
Mean: 21.7769863
SD: 2.1306152

Race

Total Asian: 22
Total Black/African American: 11
Total Caucasian/White: 34
Total Mixed Race: 21
Total Native American: 1
Total Did Not Report: 0

Ethnicity

Total Hispanic: 14
Total Not Hispanic: 75
Total Did Not Report: 0

Notes for Interpreting plot_model() Interactions with Age

For plots color-coded by Linear Age (Scaled): [-1, 0, 1] (roughly) maps to [middle-aged child participant, middle-aged adolescent participant, middle-aged adult participant]

Learning Task Plots

The Influence of Prior Reward on Future Learning Accuracy

The Influence of Reward Source Memory on Future Learning Accuracy

Median: -0.0213472

Learning Task Stats

The Influence of Prior Reward on Future Learning Accuracy

Best-fitting generalized linear mixed-effects model:
learning_acc_intercept <- glmer(correctresponses ~ instanceScaled * StimRewardType * ageScaled + instanceScaled * StimRewardType * ageScaledsq + RewardCat + (1|subID), family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=1e6)), contrasts = list(StimRewardType = "contr.sum", RewardCat = "contr.sum"), data = combined_stats)

## Data: combined_stats
## Models:
## learning_acc_agelin: correctresponses ~ instanceScaled * StimRewardType * ageScaled + 
## learning_acc_agelin:     RewardCat + (1 | subID)
## learning_acc_agesq: correctresponses ~ instanceScaled * StimRewardType * ageScaled + 
## learning_acc_agesq:     instanceScaled * StimRewardType * ageScaledsq + RewardCat + 
## learning_acc_agesq:     (1 | subID)
##                     npar   AIC   BIC  logLik deviance  Chisq Df Pr(>Chisq)  
## learning_acc_agelin   14 12778 12885 -6374.8    12750                       
## learning_acc_agesq    20 12775 12928 -6367.5    12735 14.699  6    0.02273 *
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: correctresponses
##                                              Chisq Df Pr(>Chisq)    
## (Intercept)                               188.6897  1  < 2.2e-16 ***
## instanceScaled                            230.1553  1  < 2.2e-16 ***
## StimRewardType                             30.9717  2  1.882e-07 ***
## ageScaled                                  26.2841  1  2.947e-07 ***
## ageScaledsq                                 3.6664  1   0.055521 .  
## RewardCat                                   0.4307  1   0.511627    
## instanceScaled:StimRewardType               9.7812  2   0.007517 ** 
## instanceScaled:ageScaled                   30.3191  1  3.665e-08 ***
## StimRewardType:ageScaled                    2.1142  2   0.347460    
## instanceScaled:ageScaledsq                  4.0696  1   0.043662 *  
## StimRewardType:ageScaledsq                  0.8538  2   0.652540    
## instanceScaled:StimRewardType:ageScaled     1.8437  2   0.397784    
## instanceScaled:StimRewardType:ageScaledsq   5.1871  2   0.074755 .  
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Supplemental Table 1A: Analysis of Deviance for Learning Task Performance Model Including Previously High-Reward, Previously Low-Reward, and Novel Stimuli
Predictors Chisq Df P-value
(Intercept) 188.68970 1 0.00000
Trial Number 230.15534 1 0.00000
Stimulus Type 30.97171 2 0.00000
Linear Age 26.28412 1 0.00000
Quadratic Age 3.66636 1 0.05552
High-Reward Source Image Category 0.43074 1 0.51163
Trial Number*Stimulus Type 9.78118 2 0.00752
Trial Number*Linear Age 30.31914 1 0.00000
Stimulus Type*Linear Age 2.11421 2 0.34746
Trial Number*Quadratic Age 4.06959 1 0.04366
Stimulus Type*Quadratic Age 0.85376 2 0.65254
Trial NumberStimulus TypeLinear Age 1.84369 2 0.39778
Trial NumberStimulus TypeQuadratic Age 5.18707 2 0.07476
Supplemental Table 1B: Learning Task Performance Analysis Including Previously High-Reward, Previously Low-Reward, and Novel Stimuli
Predictors Estimate Standard Error Z-value P-value
(Intercept) 2.13972 0.15577 13.73644 0.00000
Trial Number 0.54874 0.03617 15.17087 0.00000
Stimulus Type (High) -0.24422 0.04934 -4.95015 0.00000
Stimulus Type (Low) -0.00023 0.05041 -0.00457 0.99636
Linear Age 0.58397 0.11391 5.12680 0.00000
Quadratic Age -0.21394 0.11173 -1.91477 0.05552
High-Reward Source Image Category (Face) 0.07052 0.10745 0.65631 0.51163
Trial Number*Stimulus Type (High) 0.11709 0.04908 2.38569 0.01705
Trial Number*Stimulus Type (Low) 0.03567 0.05017 0.71097 0.47711
Trial Number*Linear Age 0.13438 0.02440 5.50628 0.00000
Stimulus Type (High)*Linear Age -0.03353 0.03363 -0.99675 0.31889
Stimulus Type (Low)*Linear Age -0.01709 0.03401 -0.50264 0.61522
Trial Number*Quadratic Age -0.05001 0.02479 -2.01732 0.04366
Stimulus Type (High)*Quadratic Age 0.03010 0.03410 0.88270 0.37740
Stimulus Type (Low)*Quadratic Age -0.02190 0.03454 -0.63405 0.52605
Trial NumberStimulus Type (High)Linear Age 0.03782 0.03347 1.12982 0.25855
Trial NumberStimulus Type (Low)Linear Age 0.00559 0.03386 0.16497 0.86897
Trial NumberStimulus Type (High)Quadratic Age -0.04371 0.03383 -1.29204 0.19634
Trial NumberStimulus Type (Low)Quadratic Age -0.03797 0.03429 -1.10732 0.26816

Best-fitting generalized linear mixed-effects model without new stimuli:
learning_acc_nonew_intercept <- glmer(correctresponses ~ instanceScaled * StimRewardType * ageScaled + instanceScaled * StimRewardType * ageScaledsq + RewardCat + (1|subID), family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=1e6)), contrasts = list(StimRewardType = "contr.sum", RewardCat = "contr.sum"), data = combined_stats_nonew)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: correctresponses
##                                              Chisq Df Pr(>Chisq)    
## (Intercept)                               153.3133  1  < 2.2e-16 ***
## instanceScaled                            208.6293  1  < 2.2e-16 ***
## StimRewardType                              8.1294  1   0.004355 ** 
## ageScaled                                  24.3466  1  8.047e-07 ***
## ageScaledsq                                 2.4139  1   0.120261    
## RewardCat                                   0.2908  1   0.589679    
## instanceScaled:StimRewardType               0.9208  1   0.337265    
## instanceScaled:ageScaled                   30.5287  1  3.290e-08 ***
## StimRewardType:ageScaled                    0.0950  1   0.757929    
## instanceScaled:ageScaledsq                  8.4790  1   0.003593 ** 
## StimRewardType:ageScaledsq                  0.7297  1   0.392994    
## instanceScaled:StimRewardType:ageScaled     0.3310  1   0.565097    
## instanceScaled:StimRewardType:ageScaledsq   0.0073  1   0.932127    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Supplemental Table 1C: Analysis of Deviance for Learning Task Performance Model Including Previously High-Reward and Previously Low-Reward Stimuli
Predictors Chisq Df P-value
(Intercept) 153.31331 1 0.00000
Trial Number 208.62927 1 0.00000
Stimulus Type 8.12936 1 0.00436
Linear Age 24.34660 1 0.00000
Quadratic Age 2.41392 1 0.12026
High-Reward Source Image Category 0.29085 1 0.58968
Trial Number*Stimulus Type 0.92080 1 0.33726
Trial Number*Linear Age 30.52872 1 0.00000
Stimulus Type*Linear Age 0.09499 1 0.75793
Trial Number*Quadratic Age 8.47903 1 0.00359
Stimulus Type*Quadratic Age 0.72966 1 0.39299
Trial NumberStimulus TypeLinear Age 0.33095 1 0.56510
Trial NumberStimulus TypeQuadratic Age 0.00725 1 0.93213
Supplemental Table 1D: Learning Task Performance Analysis Including Previously High-Reward and Previously Low-Reward Stimuli
Predictors Estimate Standard Error Z-value P-value
(Intercept) 1.99933 0.16147 12.38198 0.00000
Trial Number 0.61721 0.04273 14.44400 0.00000
Stimulus Type (High) -0.11995 0.04207 -2.85120 0.00436
Linear Age 0.58333 0.11822 4.93423 0.00000
Quadratic Age -0.18013 0.11594 -1.55368 0.12026
High-Reward Source Image Category (Face) 0.06010 0.11144 0.53930 0.58968
Trial Number*Stimulus Type (High) 0.04019 0.04188 0.95958 0.33726
Trial Number*Linear Age 0.16053 0.02905 5.52528 0.00000
Stimulus Type (High)*Linear Age -0.00888 0.02881 -0.30820 0.75793
Trial Number*Quadratic Age -0.08430 0.02895 -2.91188 0.00359
Stimulus Type (High)*Quadratic Age 0.02454 0.02873 0.85420 0.39299
Trial NumberStimulus Type (High)Linear Age 0.01652 0.02871 0.57529 0.56510
Trial NumberStimulus Type (High)Quadratic Age -0.00244 0.02859 -0.08517 0.93213

The Influence of Prior Reward on Future Learning Reaction Time

Best-fitting generalized linear mixed-effects model:
learning_rt_intercept <- lmer(rt ~ instanceScaled * StimRewardType * ageScaled + instanceScaled * StimRewardType * ageScaledsq + RewardCat + (1|subID), control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=1e6)), contrasts = list(StimRewardType = "contr.sum", RewardCat = "contr.sum"), data = combined_stats)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: rt
##                                              Chisq Df Pr(>Chisq)    
## (Intercept)                               572.9908  1  < 2.2e-16 ***
## instanceScaled                            465.5776  1  < 2.2e-16 ***
## StimRewardType                             14.6703  2  0.0006522 ***
## ageScaled                                   3.3848  1  0.0658017 .  
## ageScaledsq                                 4.4005  1  0.0359290 *  
## RewardCat                                   1.3106  1  0.2522846    
## instanceScaled:StimRewardType               3.3495  2  0.1873507    
## instanceScaled:ageScaled                    4.7754  1  0.0288686 *  
## StimRewardType:ageScaled                    2.5720  2  0.2763773    
## instanceScaled:ageScaledsq                 13.5671  1  0.0002302 ***
## StimRewardType:ageScaledsq                  6.5819  2  0.0372189 *  
## instanceScaled:StimRewardType:ageScaled     0.5036  2  0.7774166    
## instanceScaled:StimRewardType:ageScaledsq   9.3999  2  0.0090957 ** 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Supplemental Table 2A: Analysis of Deviance for Learning Task Reaction Time Model Including Previously High-Reward, Previously Low-Reward, and Novel Stimuli
Predictors Chisq Df P-value
(Intercept) 572.99081 1 0.00000
Trial Number 465.57760 1 0.00000
Stimulus Type 14.67032 2 0.00065
Linear Age 3.38476 1 0.06580
Quadratic Age 4.40047 1 0.03593
High-Reward Source Image Category 1.31061 1 0.25228
Trial Number*Stimulus Type 3.34955 2 0.18735
Trial Number*Linear Age 4.77543 1 0.02887
Stimulus Type*Linear Age 2.57198 2 0.27638
Trial Number*Quadratic Age 13.56714 1 0.00023
Stimulus Type*Quadratic Age 6.58187 2 0.03722
Trial NumberStimulus TypeLinear Age 0.50356 2 0.77742
Trial NumberStimulus TypeQuadratic Age 9.39991 2 0.00910
Supplemental Table 2B: Learning Task Reaction Time Analysis Including Previously High-Reward, Previously Low-Reward, and Novel Stimuli
Predictors Estimate Standard Error Df Z-value P-value
(Intercept) 673.77582 28.14761 84.98589 23.93723 0.00000
Trial Number -72.89016 3.37810 15789.00721 -21.57725 0.00000
Stimulus Type (High) 16.95137 4.76835 15789.00501 3.55497 0.00038
Stimulus Type (Low) -2.58905 4.76346 15789.00194 -0.54352 0.58678
Linear Age -38.11150 20.71534 84.99342 -1.83977 0.06929
Quadratic Age 42.55883 20.28804 84.99256 2.09773 0.03890
High-Reward Source Image Category (Face) -22.35735 19.52918 84.98997 -1.14482 0.25550
Trial Number*Stimulus Type (High) -5.28181 4.78221 15789.02402 -1.10447 0.26941
Trial Number*Stimulus Type (Low) -3.39336 4.77389 15789.01581 -0.71082 0.47721
Trial Number*Linear Age -5.44425 2.49133 15789.01565 -2.18528 0.02888
Stimulus Type (High)*Linear Age 1.24520 3.51736 15789.01750 0.35401 0.72333
Stimulus Type (Low)*Linear Age 4.13746 3.51448 15789.00673 1.17726 0.23911
Trial Number*Quadratic Age 8.96996 2.43527 15789.00586 3.68336 0.00023
Stimulus Type (High)*Quadratic Age -8.80154 3.44116 15789.01452 -2.55773 0.01055
Stimulus Type (Low)*Quadratic Age 3.80810 3.43759 15789.00981 1.10778 0.26797
Trial NumberStimulus Type (High)Linear Age -1.71030 3.52432 15789.04342 -0.48529 0.62748
Trial NumberStimulus Type (Low)Linear Age 2.43526 3.52364 15789.03123 0.69112 0.48950
Trial NumberStimulus Type (High)Quadratic Age 9.29205 3.44705 15789.04116 2.69565 0.00703
Trial NumberStimulus Type (Low)Quadratic Age -0.29352 3.44261 15789.02433 -0.08526 0.93206

Best-fitting generalized linear mixed-effects model without new stimuli:
learning_rt_nonew_intercept <- lmer(rt ~ instanceScaled * StimRewardType * ageScaled + instanceScaled * StimRewardType * ageScaledsq + RewardCat + (1|subID), control = lmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=1e6)), contrasts = list(StimRewardType = "contr.sum", RewardCat = "contr.sum"), data = combined_stats_nonew)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: rt
##                                              Chisq Df Pr(>Chisq)    
## (Intercept)                               576.9664  1  < 2.2e-16 ***
## instanceScaled                            343.1541  1  < 2.2e-16 ***
## StimRewardType                              5.5104  1    0.01890 *  
## ageScaled                                   2.8830  1    0.08952 .  
## ageScaledsq                                 3.8482  1    0.04980 *  
## RewardCat                                   1.2867  1    0.25665    
## instanceScaled:StimRewardType               0.0517  1    0.82005    
## instanceScaled:ageScaled                    2.7274  1    0.09864 .  
## StimRewardType:ageScaled                    0.2230  1    0.63673    
## instanceScaled:ageScaledsq                 20.0591  1  7.509e-06 ***
## StimRewardType:ageScaledsq                  4.4122  1    0.03568 *  
## instanceScaled:StimRewardType:ageScaled     0.4649  1    0.49534    
## instanceScaled:StimRewardType:ageScaledsq   2.5608  1    0.10954    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Supplemental Table 2C: Analysis of Deviance for Learning Task Reaction Time Model Including Previously High-Reward and Previously Low-Reward Stimuli
Predictors Chisq Df P-value
(Intercept) 576.96636 1 0.00000
Trial Number 343.15409 1 0.00000
Stimulus Type 5.51042 1 0.01890
Linear Age 2.88299 1 0.08952
Quadratic Age 3.84817 1 0.04980
High-Reward Source Image Category 1.28671 1 0.25665
Trial Number*Stimulus Type 0.05175 1 0.82005
Trial Number*Linear Age 2.72745 1 0.09864
Stimulus Type*Linear Age 0.22305 1 0.63673
Trial Number*Quadratic Age 20.05910 1 0.00001
Stimulus Type*Quadratic Age 4.41225 1 0.03568
Trial NumberStimulus TypeLinear Age 0.46491 1 0.49534
Trial NumberStimulus TypeQuadratic Age 2.56080 1 0.10954
Supplemental Table 2D: Learning Task Reaction Time Analysis Including Previously High-Reward and Previously Low-Reward Stimuli
Predictors Estimate Standard Error Df Z-value P-value
(Intercept) 680.94508 28.34894 84.97405 24.02012 0.00000
Trial Number -77.20640 4.16782 10497.00946 -18.52442 0.00000
Stimulus Type (High) 9.75794 4.15687 10497.00012 2.34743 0.01892
Linear Age -35.42598 20.86412 84.98628 -1.69794 0.09318
Quadratic Age 40.08517 20.43415 84.98680 1.96168 0.05307
High-Reward Source Image Category (Face) -22.31115 19.66894 84.97950 -1.13433 0.25984
Trial Number*Stimulus Type (High) -0.94814 4.16790 10497.02800 -0.22749 0.82005
Trial Number*Linear Age -5.07682 3.07407 10497.01924 -1.65150 0.09867
Stimulus Type (High)*Linear Age -1.44839 3.06681 10497.01627 -0.47228 0.63674
Trial Number*Quadratic Age 13.45931 3.00516 10497.00131 4.47874 0.00001
Stimulus Type (High)*Quadratic Age -6.30271 3.00053 10497.01719 -2.10053 0.03571
Trial NumberStimulus Type (High)Linear Age -2.09612 3.07420 10497.05723 -0.68184 0.49535
Trial NumberStimulus Type (High)Quadratic Age 4.80925 3.00531 10497.05176 1.60025 0.10957

The Influence of Reward Source Memory on Future Learning Accuracy

Best-fitting regression:
mem_regression_agesq <- lm(HighRewSourceMemBenefitDay2 ~ ageScaled + ageScaledsq + RewardCat, data = retData)

## Analysis of Variance Table
## 
## Model 1: HighRewSourceMemBenefitDay2 ~ ageScaled + RewardCat
## Model 2: HighRewSourceMemBenefitDay2 ~ ageScaled + ageScaledsq + RewardCat
##   Res.Df     RSS Df Sum of Sq      F   Pr(>F)   
## 1     86 0.57848                                
## 2     85 0.52733  1  0.051148 8.2445 0.005159 **
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Call:
## lm(formula = HighRewSourceMemBenefitDay2 ~ ageScaled + ageScaledsq + 
##     RewardCat, data = retData_withRewardCat)
## 
## Residuals:
##       Min        1Q    Median        3Q       Max 
## -0.259162 -0.046011 -0.000952  0.059214  0.194452 
## 
## Coefficients:
##                  Estimate Std. Error t value Pr(>|t|)   
## (Intercept)      0.012198   0.014957   0.816  0.41704   
## ageScaled        0.010930   0.008919   1.226  0.22377   
## ageScaledsq     -0.025222   0.008784  -2.871  0.00516 **
## RewardCatPlaces -0.041920   0.016722  -2.507  0.01409 * 
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.07876 on 85 degrees of freedom
## Multiple R-squared:  0.1412, Adjusted R-squared:  0.1109 
## F-statistic: 4.658 on 3 and 85 DF,  p-value: 0.004611

Best-fitting generalized linear mixed-effects model:
Quadratic age model did not converge.

Best-fitting generalized linear mixed-effects model without new stimuli:
learning_source_nonew_intercept <- glmer(correctresponses ~ instanceScaled * StimRewardType * ageScaled * HighRewSourceMemBenefitDay2 + instanceScaled * StimRewardType * ageScaledsq * HighRewSourceMemBenefitDay2 + RewardCat + (1|subID), family = binomial, control = glmerControl(optimizer = "bobyqa", optCtrl = list(maxfun=1e6)), contrasts = list(StimRewardType = "contr.sum", RewardCat = "contr.sum"), data = combined_stats_nonew)

## Analysis of Deviance Table (Type III Wald chisquare tests)
## 
## Response: correctresponses
##                                                                          Chisq
## (Intercept)                                                           151.1378
## instanceScaled                                                        211.8034
## StimRewardType                                                          9.9123
## ageScaled                                                              24.1065
## HighRewSourceMemBenefitDay2                                             0.1561
## ageScaledsq                                                             1.0092
## RewardCat                                                               0.1577
## instanceScaled:StimRewardType                                           0.5342
## instanceScaled:ageScaled                                               15.4828
## StimRewardType:ageScaled                                                0.0187
## instanceScaled:HighRewSourceMemBenefitDay2                              7.0770
## StimRewardType:HighRewSourceMemBenefitDay2                              2.4363
## ageScaled:HighRewSourceMemBenefitDay2                                   2.0082
## instanceScaled:ageScaledsq                                              8.7250
## StimRewardType:ageScaledsq                                              3.4989
## HighRewSourceMemBenefitDay2:ageScaledsq                                 0.0860
## instanceScaled:StimRewardType:ageScaled                                 0.3246
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2               0.2737
## instanceScaled:ageScaled:HighRewSourceMemBenefitDay2                    0.0000
## StimRewardType:ageScaled:HighRewSourceMemBenefitDay2                    0.0000
## instanceScaled:StimRewardType:ageScaledsq                               0.5469
## instanceScaled:HighRewSourceMemBenefitDay2:ageScaledsq                  5.3235
## StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq                  6.2008
## instanceScaled:StimRewardType:ageScaled:HighRewSourceMemBenefitDay2     0.0180
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq   1.9671
##                                                                       Df
## (Intercept)                                                            1
## instanceScaled                                                         1
## StimRewardType                                                         1
## ageScaled                                                              1
## HighRewSourceMemBenefitDay2                                            1
## ageScaledsq                                                            1
## RewardCat                                                              1
## instanceScaled:StimRewardType                                          1
## instanceScaled:ageScaled                                               1
## StimRewardType:ageScaled                                               1
## instanceScaled:HighRewSourceMemBenefitDay2                             1
## StimRewardType:HighRewSourceMemBenefitDay2                             1
## ageScaled:HighRewSourceMemBenefitDay2                                  1
## instanceScaled:ageScaledsq                                             1
## StimRewardType:ageScaledsq                                             1
## HighRewSourceMemBenefitDay2:ageScaledsq                                1
## instanceScaled:StimRewardType:ageScaled                                1
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2              1
## instanceScaled:ageScaled:HighRewSourceMemBenefitDay2                   1
## StimRewardType:ageScaled:HighRewSourceMemBenefitDay2                   1
## instanceScaled:StimRewardType:ageScaledsq                              1
## instanceScaled:HighRewSourceMemBenefitDay2:ageScaledsq                 1
## StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq                 1
## instanceScaled:StimRewardType:ageScaled:HighRewSourceMemBenefitDay2    1
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq  1
##                                                                       Pr(>Chisq)
## (Intercept)                                                            < 2.2e-16
## instanceScaled                                                         < 2.2e-16
## StimRewardType                                                          0.001642
## ageScaled                                                              9.115e-07
## HighRewSourceMemBenefitDay2                                             0.692781
## ageScaledsq                                                             0.315090
## RewardCat                                                               0.691264
## instanceScaled:StimRewardType                                           0.464843
## instanceScaled:ageScaled                                               8.326e-05
## StimRewardType:ageScaled                                                0.891267
## instanceScaled:HighRewSourceMemBenefitDay2                              0.007808
## StimRewardType:HighRewSourceMemBenefitDay2                              0.118555
## ageScaled:HighRewSourceMemBenefitDay2                                   0.156453
## instanceScaled:ageScaledsq                                              0.003139
## StimRewardType:ageScaledsq                                              0.061408
## HighRewSourceMemBenefitDay2:ageScaledsq                                 0.769309
## instanceScaled:StimRewardType:ageScaled                                 0.568871
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2               0.600834
## instanceScaled:ageScaled:HighRewSourceMemBenefitDay2                    0.995279
## StimRewardType:ageScaled:HighRewSourceMemBenefitDay2                    0.996765
## instanceScaled:StimRewardType:ageScaledsq                               0.459601
## instanceScaled:HighRewSourceMemBenefitDay2:ageScaledsq                  0.021040
## StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq                  0.012769
## instanceScaled:StimRewardType:ageScaled:HighRewSourceMemBenefitDay2     0.893245
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq   0.160751
##                                                                          
## (Intercept)                                                           ***
## instanceScaled                                                        ***
## StimRewardType                                                        ** 
## ageScaled                                                             ***
## HighRewSourceMemBenefitDay2                                              
## ageScaledsq                                                              
## RewardCat                                                                
## instanceScaled:StimRewardType                                            
## instanceScaled:ageScaled                                              ***
## StimRewardType:ageScaled                                                 
## instanceScaled:HighRewSourceMemBenefitDay2                            ** 
## StimRewardType:HighRewSourceMemBenefitDay2                               
## ageScaled:HighRewSourceMemBenefitDay2                                    
## instanceScaled:ageScaledsq                                            ** 
## StimRewardType:ageScaledsq                                            .  
## HighRewSourceMemBenefitDay2:ageScaledsq                                  
## instanceScaled:StimRewardType:ageScaled                                  
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2                
## instanceScaled:ageScaled:HighRewSourceMemBenefitDay2                     
## StimRewardType:ageScaled:HighRewSourceMemBenefitDay2                     
## instanceScaled:StimRewardType:ageScaledsq                                
## instanceScaled:HighRewSourceMemBenefitDay2:ageScaledsq                *  
## StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq                *  
## instanceScaled:StimRewardType:ageScaled:HighRewSourceMemBenefitDay2      
## instanceScaled:StimRewardType:HighRewSourceMemBenefitDay2:ageScaledsq    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
Supplemental Table 3A: Analysis of Deviance for Individual Differences in Learning Model Including Previously High-Reward and Previously Low-Reward Stimuli
Predictors Chisq Df P-value
(Intercept) 151.13778 1 0.00000
Trial Number 211.80338 1 0.00000
Stimulus Type 9.91231 1 0.00164
Linear Age 24.10652 1 0.00000
General Reward Source Memory 0.15609 1 0.69278
Quadratic Age 1.00922 1 0.31509
High-Reward Source Image Category 0.15772 1 0.69126
Trial Number*Stimulus Type 0.53421 1 0.46484
Trial Number*Linear Age 15.48283 1 0.00008
Stimulus Type*Linear Age 0.01869 1 0.89127
Trial Number*General Reward Source Memory 7.07697 1 0.00781
Stimulus Type*General Reward Source Memory 2.43631 1 0.11855
Linear Age*General Reward Source Memory 2.00818 1 0.15645
Trial Number*Quadratic Age 8.72505 1 0.00314
Stimulus Type*Quadratic Age 3.49895 1 0.06141
General Reward Source Memory*Quadratic Age 0.08601 1 0.76931
Trial NumberStimulus TypeLinear Age 0.32458 1 0.56887
Trial NumberStimulus TypeGeneral Reward Source Memory 0.27374 1 0.60083
Trial NumberLinear AgeGeneral Reward Source Memory 0.00004 1 0.99528
Stimulus TypeLinear AgeGeneral Reward Source Memory 0.00002 1 0.99677
Trial NumberStimulus TypeQuadratic Age 0.54687 1 0.45960
Trial NumberGeneral Reward Source MemoryQuadratic Age 5.32352 1 0.02104
Stimulus TypeGeneral Reward Source MemoryQuadratic Age 6.20079 1 0.01277
Trial NumberStimulus TypeLinear Age*General Reward Source Memory 0.01801 1 0.89324
Trial NumberStimulus TypeGeneral Reward Source Memory*Quadratic Age 1.96715 1 0.16075
Supplemental Table 3B: Individual Differences in Learning Analysis Including Previously High-Reward and Previously Low-Reward Stimuli
Predictors Estimate Standard Error Z-value P-value
(Intercept) 2.00702 0.16325 12.29381 0.00000
Trial Number 0.64288 0.04417 14.55347 0.00000
Stimulus Type (High) -0.13716 0.04357 -3.14838 0.00164
Linear Age 0.67534 0.13755 4.90984 0.00000
General Reward Source Memory 0.93852 2.37550 0.39508 0.69278
Quadratic Age -0.13328 0.13267 -1.00460 0.31509
High-Reward Source Image Category (Face) 0.04539 0.11429 0.39714 0.69126
Trial Number*Stimulus Type (High) 0.03162 0.04326 0.73090 0.46484
Trial Number*Linear Age 0.14337 0.03644 3.93482 0.00008
Stimulus Type (High)*Linear Age 0.00498 0.03644 0.13670 0.89127
Trial Number*General Reward Source Memory 1.62006 0.60899 2.66026 0.00781
Stimulus Type (High)*General Reward Source Memory -0.93768 0.60074 -1.56087 0.11855
Linear Age*General Reward Source Memory 1.90960 1.34754 1.41710 0.15645
Trial Number*Quadratic Age -0.10156 0.03438 -2.95382 0.00314
Stimulus Type (High)*Quadratic Age 0.06425 0.03435 1.87055 0.06141
General Reward Source Memory*Quadratic Age 0.43735 1.49123 0.29328 0.76931
Trial NumberStimulus Type (High)Linear Age 0.02055 0.03606 0.56972 0.56887
Trial NumberStimulus Type (High)General Reward Source Memory -0.31187 0.59609 -0.52320 0.60083
Trial NumberLinear AgeGeneral Reward Source Memory 0.00217 0.36650 0.00592 0.99528
Stimulus Type (High)Linear AgeGeneral Reward Source Memory -0.00148 0.36589 -0.00405 0.99677
Trial NumberStimulus Type (High)Quadratic Age 0.02514 0.03399 0.73950 0.45960
Trial NumberGeneral Reward Source MemoryQuadratic Age -0.86519 0.37498 -2.30727 0.02104
Stimulus Type (High)General Reward Source MemoryQuadratic Age 0.92593 0.37184 2.49014 0.01277
Trial NumberStimulus Type (High)Linear Age*General Reward Source Memory -0.04866 0.36256 -0.13420 0.89324
Trial NumberStimulus Type (High)General Reward Source Memory*Quadratic Age 0.51975 0.37057 1.40255 0.16075

Test Phase Plots

The Influence of Prior Reward on Test Phase Choices

Test Phase Stats

The Influence of Prior Reward on Test Phase Choices

Regressions:
HH_HHLH_regression <- lm(HH_HHLH_mean ~ ageScaled + reward_cat, data = test_graph)
HL_HLLL_regression <- lm(HL_HLLL_mean ~ ageScaled + reward_cat, data = test_graph)

## 
## Call:
## lm(formula = HH_HHLH_mean ~ ageScaled + reward_cat, data = test_graph)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.55294 -0.27636 -0.04789  0.28187  0.63206 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.40975    0.05271   7.774 1.51e-11 ***
## ageScaled       -0.03075    0.03727  -0.825    0.412    
## reward_catscene  0.10812    0.07413   1.458    0.148    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3496 on 86 degrees of freedom
## Multiple R-squared:  0.03219,    Adjusted R-squared:  0.009685 
## F-statistic:  1.43 on 2 and 86 DF,  p-value: 0.2449
Supplemental Table 4A: Test Phase Analysis for 70%:70% Pairings
Predictors Estimate Standard Error T-value P-value
(Intercept) 0.40975 0.05271 7.77400 0.00000
Linear Age -0.03075 0.03727 -0.82506 0.41162
High-Reward Source Image Category (Place) 0.10812 0.07413 1.45849 0.14835 
## 
## Call:
## lm(formula = HL_HLLL_mean ~ ageScaled + reward_cat, data = test_graph)
## 
## Residuals:
##      Min       1Q   Median       3Q      Max 
## -0.54134 -0.25132 -0.01396  0.24320  0.59299 
## 
## Coefficients:
##                 Estimate Std. Error t value Pr(>|t|)    
## (Intercept)      0.43590    0.04815   9.053 3.84e-14 ***
## ageScaled       -0.01377    0.03405  -0.404    0.687    
## reward_catscene  0.08974    0.06772   1.325    0.189    
## ---
## Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
## 
## Residual standard error: 0.3193 on 86 degrees of freedom
## Multiple R-squared:  0.0221, Adjusted R-squared:  -0.0006468 
## F-statistic: 0.9716 on 2 and 86 DF,  p-value: 0.3826
Supplemental Table 4B: Test Phase Analysis for 30%:30% Pairings
Predictors Estimate Standard Error T-value P-value
(Intercept) 0.43590 0.04815 9.05313 0.00000
Linear Age -0.01377 0.03405 -0.40444 0.68689
High-Reward Source Image Category (Place) 0.08974 0.06772 1.32506 0.18866